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3D Scene Calibration for Infrared Image Analysis

3D Scene Calibration for Infrared Image Analysis. V. Martin , V. Gervaise, V. Moncada, M.H. Aumeunier, M. Firdaouss, J.M. Travere (CEA) S. Devaux (IPP), G. Arnoux (CCFE) and JET-EFDA contributors Workshop on Fusion Data Processing Validation and Analysis, ENEA Frascati, 26-28 March 2012.

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3D Scene Calibration for Infrared Image Analysis

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  1. 3D Scene Calibration for Infrared Image Analysis V. Martin, V. Gervaise, V. Moncada, M.H. Aumeunier, M. Firdaouss, J.M. Travere (CEA) S. Devaux (IPP), G. Arnoux (CCFE) and JET-EFDA contributors Workshop on Fusion Data Processing Validation and Analysis, ENEA Frascati, 26-28 March 2012

  2. 3D IR Scene Calibration JET #81313 KL7 (images in DL) • Issue: a complex thermal scene • Wide angle views with high geometrical effects: depth of field and curvature • Many metallic materials (Be, W) with different and changing optical (reflectance) and thermal (emissivity) properties • Objective: Match each pixel with the 3D scene model of in-vessel components for: • getting the real geometry of the viewed objects • reliable linking between viewed objects and their related properties • Applications • Image processing (e.g. event characterization) • IR data calibration: Tsurf = f(material emissivity) Bulk Be W coated CFC Bulk Be Bulk Be Be coated linconel W coated CFC Bulk W

  3. Methodology • Calibration chain NUC Dead pixel Map Reference image 2D/3D scene models Knowledge base of the thermal scene Image Correction Image Stabilization 2D/3D Scene Model Mapping Image Processing Registered & Geo-calibrated Image Camera

  4. Illustration of Motion in Images • Camera vibrations lead to misalignments of ROIs (PFC RT protection) = false alarms or worth missed alarms • Image stabilization is a mandatory step for heat flux deposit analysis based on Tsurf(t)-Tsurf(t-1) estimations

  5. Image Stabilization • Important factors for method selection • Deformation type: planar (homothety), non-planar • Target application: real-time processing, off-line analysis • Data quality and variability: noise level, pixel intensity changes, image entropy • Required precision level: pixel, sub-pixel • Applications in tokamaks (non-exhaustive list)

  6. Image Stabilization • Classical Methodology • Feature Detection • Local descriptors: Harris corners, MSER, codebooks, Gabor wavelets (see Craciunescu talk), SIFT, SURF, FAST… • Global descriptors: Tsallis entropy (see Murari talk), edge detectors… • Fourier analysis: spectral magnitude & phase, pixel gradients, log-polar mapping… • Feature Matching • Spatial cross-correlation techniques: normalized cross-correlation, Hausdorff distance… • Fourier domain: normalized cross-spectrum and its extensions • Transform Model Estimation • Shape preserving mapping (rotation, translation and scaling only) • Elastic mapping: warping techniques… • Image transformation • 2D Interpolation: nearest neighboor, bilinear, bicubic… See Zitova’s survey, Image and Vision Computing, vol. 21(2003), pp. 977-1000

  7. Proposed Algorithm • Masked FFT-based image registration [1] • Deterministic computing time • Accelerating hardware compatible algorithm (e.g. FFT on GPU) → real time applications • Local analysis with dynamic intensity-based pixel masking (e.g. mask the divertor bright region) • with sub-pixel precision [2] • Slow drift compensation • and dynamic update of the reference image • Robust to image intensity and structural changes • Evaluation of the registration quality over time [1] D. Padfield, IEEE CVPR’10, pp. 2918-2925, 2010 [2] M. Guizar-Sicairos et al., Opt. Lett., vol. 33, no. 2, pp. 156-158, 2008

  8. Principle of Fourier-based Correlation Iref It NCC(Iref, It) max (NCC(Iref, It)) • Let Iref a reference image, It an image at time t and DFT the Discrete 2D Fourier transform such as It ( x , y ) = Iref ( x-x0 , y-y0 ) • NCC is the Normalized Cross Correlation figure (image) and the position of the peak gives the coordinates of the translation ( x0 , y0 )

  9. Sub-pixel Precision • Up-sample k times the DFT of NCC (trigonometric interpolation): • The peak coordinates ( x0 , y0 ) give F the translation with 1/k pixel of precision:

  10. Reference Image Updating • Goal: maintaining a good reliability of the motion estimator (NCC peak value) while image appearance changes during the pulse.

  11. Reference Image Updating • Solution: use the NCC peak value to trigger the update of Iref such as: updateIref updateIref updateIref updateIref NCC peak too low, no Iref update

  12. Results • JET #81313 (MARFE, disruption), KL7, 480x512 pixels, 50 Hz, 251 frames k=1/4 pixel

  13. Results • JET #80827 (disruption), KL7, 128x256 pixels, 540 Hz, 13425 frames k=1/2 pixel

  14. Results • JET #82278, KL9B (slow drift), 32x96 pixels, 6 kHz, 4828 frames 96 pixels 32 pixels

  15. Computational Performance • High frame rate performance using GPU 256x256, k=1/4 → 700 fps

  16. From 2D to 3D • Challenge • transform pixel coordinates into machine coordinates: (x, y)  (r, θ, φ) • Method • Ray-tracing method from 3D/simplified CAD files

  17. 3D Scene Model for Image Processing S. Palazzo, A. Murari et al., RSI 81, 083505, 2010 Z Map (depth) 1 mm 2 2m 1 Blobs 1 & 2 must not be merged! 7m 2 1 2 V. Martin et al.

  18. Integrated Framework NUC Dead pixel Map Reference image 2D/3D scene models Knowledge base of the thermal scene Load/save translations Used for event triggering Set mask Image Correction Image Stabilization 2D/3D Scene Model Mapping Image Processing Registered & Calibrated Image Used for temperature evaluation Camera Set sub-pixel precision factor Used for PFC protection • An integrated software for IR data stabilization & analysis Plasma ImagiNg data Understanding Platform (PINUP)

  19. Conclusion • Summary • Complex IR scenes require a new approach for reliable data analysis including image stabilization and 3D mapping. • A robust and fast image stabilization algorithm with sub-pixel precision has been proposed. • A first demonstration of 3D model for IR data analysis has been successfully carried out at JET on the wide-angle ITER-like viewing system (KL7). • An integrated software (PINUP) implementing these features is available for users upon request. • Outlook • Test of the stabilization algorithm on visible imaging data (JET KL8) with rotation compensation • Full integration of 3D scene models into PINUP • Improvement of image processing algorithms (e.g. hot spot detection) with 3D information

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